U.S. patent application number 13/061050 was filed with the patent office on 2011-12-22 for utility data processing system.
This patent application is currently assigned to ONZO LIMITED. Invention is credited to Jose Manuel Sanchey Loureda, Adam Richard WestBrooke.
Application Number | 20110313964 13/061050 |
Document ID | / |
Family ID | 41717197 |
Filed Date | 2011-12-22 |
United States Patent
Application |
20110313964 |
Kind Code |
A1 |
Sanchey Loureda; Jose Manuel ;
et al. |
December 22, 2011 |
UTILITY DATA PROCESSING SYSTEM
Abstract
A utility data processing system for processing data relating to
consumption of a utility comprises: a fact memory for storage of
facts relating to utility consumption received from fact sources,
at least one fact source module for deriving facts from utility
consumption data and adding the derived facts to the tact memory,
an inference module for inferring new facts relating to utility
consumption from one or more facts stored in the fact memory, and
an interlace module.
Inventors: |
Sanchey Loureda; Jose Manuel;
(London, GB) ; WestBrooke; Adam Richard; (Kent,
GB) |
Assignee: |
ONZO LIMITED
London
GB
|
Family ID: |
41717197 |
Appl. No.: |
13/061050 |
Filed: |
December 20, 2010 |
PCT Filed: |
December 20, 2010 |
PCT NO: |
PCT/GB10/52157 |
371 Date: |
September 6, 2011 |
Current U.S.
Class: |
706/50 |
Current CPC
Class: |
H02J 2310/70 20200101;
Y04S 20/30 20130101; G06Q 10/04 20130101; Y02B 90/20 20130101; H02J
3/003 20200101; Y02B 70/3225 20130101; H02J 2310/14 20200101; Y04S
20/242 20130101; G01D 4/002 20130101; H02J 3/14 20130101; Y04S
20/222 20130101; G06Q 50/06 20130101; Y02B 70/30 20130101; H02J
2310/64 20200101; Y04S 50/10 20130101 |
Class at
Publication: |
706/50 |
International
Class: |
G06N 5/02 20060101
G06N005/02 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 18, 2009 |
GB |
0922164.9 |
Claims
1. A utility data processing system for processing data relating to
consumption of a utility, the system comprising: a fact memory for
storage of facts relating to utility consumption received from fact
sources; at least one fact source module for deriving facts from
utility consumption data and adding the derived facts to the fact
memory; an inference module for inferring new facts relating to
utility consumption from one or more facts stored in the fact
memory; and an interface module.
2. A system according to claim 1 wherein one of the at least one
fact source modules comprises an appliance identification module
configured to identify one or more appliances using data relating
to utility consumption by the one or more appliances, and to add
the identity of the one or more appliances as one or more facts in
the fact memory.
3. A system according to claim 2 wherein the appliance
identification module comprises: a profile generator for generating
a utility consumption profile from utility consumption data, the
utility consumption data comprising a plurality of utility
consumption values measured at a corresponding plurality of
measurement points; and an event identifier for identifying an
event within the utility consumption profile that matches the
profile of a known event associated with operation of a known
device, said known event stored in a database of utility
consumption profiles.
4. A system according to claim 2 wherein the appliance
identification module is further configured to add the time of use
or duration of use the one or more appliances present within the
household as one or more facts in the fact memory.
5. A system according to claim 1 wherein the at least one fact
source module comprises a statistics module configured to generate
statistical facts on energy consumption from the utility
consumption data.
6. A system according to claim 5 wherein the statistical facts are
selected from: energy use per period of time; variations in energy
usage per period of time; and baseload energy.
7. A system according to claim 6 wherein the period of time is one
or more of 1 hour, 1 day, 1 week, 1 month and 1 year.
8. (canceled)
9. A system according to claim 1 further comprising a data memory
for storage of utility consumption data received from at least one
utility meter.
10. A system according to claim 1 wherein the interface module is a
presentation interface configured to present facts on a display
screen.
11. A system according to claim 10 wherein the system is configured
to receive data from the presentation interface.
12. A system according to claim 10 wherein the fact memory is
configured to receive facts declared by a user of the system via
the interface.
13. A system according to claim 12 wherein the system is configured
to prompt the declaration of a fact by a user.
14. A system according to claim 1 wherein the interface module is a
control interface.
15. A system according to claim 14 wherein the control interface is
configured to control utility consumption within a household
managed by the utility data processing system based on control
parameters determined by one or more facts.
16. A system according to claim 5 wherein the statistics module is
configured to generate average utility consumption by a group of
households.
17. A system according to claim 2 wherein the inference module is
configured to identify appliances for the appliance detection
module to search for.
18. A method of processing utility consumption data comprising the
steps of: receiving utility consumption data; deriving at least one
fact from utility consumption data; storing the at least one fact
in a fact memory; and inferring at least one new fact from the at
least one fact stored in the fact memory.
19. (canceled)
20. (canceled)
21. (canceled)
22. An article of manufacture comprising: a machine-readable
storage medium; and executable program instructions embodied in the
machine readable storage medium that when executed by a
programmable system causes the system to perform the function of
processing utility consumption data comprising the steps of:
receiving utility consumption data; deriving at least one fact from
utility consumption data; storing the at least one fact in a fact
memory; and inferring at least one new fact from the at least one
fact stored in the fact memory.
23. A utility data processing data system for deriving facts
relating to consumption of a utility comprising: a fact memory for
storage of facts relating to utility consumption received from one
or more fact sources; wherein one said fact source comprises an
inference module configured to infer new facts relating to utility
consumption from one or more facts stored in the fact memory; and a
question management module configured to communicate a request to a
system user to provide one or more facts not available from any
other fact source.
24. A method of processing utility consumption data comprising the
steps of: analysing facts relating to utility consumption stored in
a fact memory to identify one or more required facts not contained
within the fact memory; and if one or more required facts is not
contained within the fact memory, communicating a request to a
system user to provide the one or more facts.
25. (canceled)
26. (canceled)
27. (canceled)
28. An article of manufacture comprising: a machine-readable
storage medium; and executable program instructions embodied in the
machine readable storage medium that when executed by a
programmable system causes the system to perform the function of
processing utility consumption data comprising the steps of:
analysing facts relating to utility consumption stored in a fact
memory to identify one or more required facts not contained within
the fact memory; and if one or more required facts is not contained
within the fact memory, communicating a request to a system user to
provide the one or more facts.
Description
FIELD OF THE INVENTION
[0001] This invention relates to methods, systems, devices and
computer code for processing of utility consumption data and
managing consumption of utilities, in particular consumption of
gas, water and electricity, and using measured utility consumption
data to generate facts relating to utility consumption.
BACKGROUND
[0002] There is an ongoing and urgent need to reduce consumption of
electricity, gas and water both for environmental and cost
reasons.
[0003] A large proportion of the electrical energy, gas and water
supplied by utilities suppliers is wasted as a result of
inefficiencies such as use of electrical appliances that have poor
efficiency or for behavioural reasons such as appliances that are
left switched on and so consume electricity even when not in use,
or consumption of water than is actually needed. This leads to
wastage and increased utilities costs. Moreover, with respect to
electricity, electrical energy use in buildings accounts for a very
large proportion of all carbon emissions. Demand for utilities can
vary dramatically between identical buildings with the same number
of occupants, and this suggests that reducing waste through
behavioural efficiency is essential. Therefore, efforts are
required to change the patterns of utilities use by consumers.
[0004] The utilities suppliers recognise three major obstacles to
progress in this objective: a shortage of sources of competitive
advantage, a lack of detailed understanding of their customers, and
a lack of "touch points", i.e. ways of interacting with the
customers. Opportunities for differentiation revolve mainly around
price and "green" issues, i.e. reduction of environmental impact.
The utilities suppliers have very little information about their
customers' behaviour since electricity, gas and water meters
collect whole house data continuously and are read
infrequently.
[0005] Meters to measure total consumption of utilities of a
household are commonplace for each of gas, electricity and water,
however this total is not useful in identifying areas in which
efficiencies may be possible (for brevity, we refer herein to a
"household", however it will be appreciated that the present
invention is not limited to a domestic house but may be applied to
any domestic, workplace or other setting that receives its own
discrete utilities supplies, in particular mains electricity supply
from an electricity grid; water supply; and/or gas supply.).
[0006] Detailed information on utility consumption may be provided
by a system comprising a measuring device which may be a standard
utility meter, a Smart meter, or an sensor additional to the meter
or a datalogger attached to the meter (for example to read pulse
output counts).
[0007] These systems contain a pathway for returning data to a
utility supplier or a utility service provider, such as a return
path as in advanced metering infrastructure (AMI) or automatic
meter reading (AMR) systems. Alternatively, upload of data may be
via the internet through a gateway within the household, and/or via
a display or device that logs data and can upload data from time to
time by wired or wireless communication means.
[0008] Data return may be under the utility provider's control
and/or under the consumer's control.
[0009] For example, a smart energy kit is available from Onzo
Limited in which electricity consumption data measured by an
electricity sensor connectable to a conventional electricity meter
is wirelessly transmitted to a user display that displays data such
as current power consumption, daily total energy consumption,
comparison to previous weeks, target energy consumption, cost of
electricity consumed and alerts when usage is high and/or at times
when national grid demand is high. This data may be uploaded via
the internet to a server for analysis of utility consumption.
[0010] While metering of this type provide a simple and effective
way of communicating detailed information on utility consumption,
the information that such meters generate is based only on the
directly measured electricity consumption data.
[0011] In order to develop a more detailed understanding of utility
consumption within a household, household occupants may also be
requested to answer a questionnaire including questions such as the
household location, household size, number of occupants, appliances
within the household, etc., although answering such a questionnaire
to the level of detail required to provide improved utility
consumption data can be very time consuming and inconvenient.
Moreover, the answers given to such questions may change over
time.
[0012] It is therefore an object of the invention to provide a
user-friendly means of generating detailed and accurate information
on utility consumption.
SUMMARY OF THE INVENTION
[0013] The present inventors have developed a utility data
processing system that does not necessarily require user input in
order to provide a user with detailed information on utility
consumption, and/or to enable the system to directly control
appliances within a household in order to optimise utility
consumption of those appliances.
[0014] "Utility" as used herein may be any delivered supply capable
of being metered, including electricity, gas and water supply.
[0015] For brevity, we refer herein to a "household", however it
will be appreciated that the present invention is not limited to a
domestic house but may be applied to any domestic, workplace or
other setting that receives its own discrete utilities supplies, in
particular mains electricity supply from an electricity grid; water
supply; and/or gas supply.
[0016] Accordingly, in a first aspect the invention provides a
utility data processing system for processing data relating to
consumption of a utility comprising: [0017] a fact memory for
storage of facts relating to utility consumption received from fact
sources; [0018] at least one fact source module for deriving facts
from utility consumption data and adding the derived facts to the
fact memory; and [0019] an inference module for inferring new facts
relating to utility consumption from one or more facts stored in
the fact memory; and [0020] an interface module.
[0021] Optionally, one of the at least one fact source modules
comprises an appliance identification module configured to identify
one or more appliances using data relating to utility consumption
by the one or more appliances, and to add the identity of the one
or more appliances as one or more facts in the fact memory.
[0022] Optionally, the appliance identification module comprises:
[0023] a profile generator for generating a utility consumption
profile from utility consumption data, the utility consumption data
comprising a plurality of utility consumption values measured at a
corresponding plurality of measurement points; and [0024] an event
identifier for identifying an event within the utility consumption
profile that matches the profile of a known event associated with
operation of a known device, said known event stored in a database
of utility consumption profiles.
[0025] Optionally, the appliance identification module is further
configured to add the time of use and/or duration of use the one or
more appliances present within the household as one or more facts
in the fact memory.
[0026] Optionally, the at least one fact source module comprises a
statistics module configured to generate statistical facts on
energy consumption from the utility consumption data.
[0027] Optionally, the statistical facts are selected from: energy
use per period of time; variations in energy usage per period of
time; and baseload energy.
[0028] Optionally, the period of time is one or more of 1 hour, 1
day, 1 week, 1 month and 1 year.
[0029] Optionally, the system comprises an appliance identification
module and a statistics module.
[0030] Optionally, the system further comprises a data memory for
storage of utility consumption data received from at least one
utility meter.
[0031] Optionally, the interface is a presentation interface
configured to present facts on a display screen.
[0032] Optionally, the system is configured to receive data from
the presentation interface.
[0033] Optionally, the fact memory is configured to receive facts
declared by a user of the system via the interface.
[0034] Optionally, the system is configured to prompt the
declaration of a fact by a user.
[0035] Optionally, the interface is a control interface.
[0036] Optionally, the control interface is configured to control
utility consumption within a household managed by the utility data
processing system based on control parameters determined by one or
more facts.
[0037] Optionally, the statistics module is configured to generate
average utility consumption by a group of households.
[0038] Optionally, the inference module is configured to identify
appliances for the appliance detection module to search for.
[0039] In a second aspect, the invention provides a method of
processing utility consumption data comprising the steps of: [0040]
receiving utility consumption data; [0041] deriving at least one
fact from utility consumption data; [0042] storing the at least one
fact in a fact memory; and [0043] inferring at least one new fact
from the at least one fact stored in the utility consumption
data.
[0044] In a third aspect, the invention provides computer program
code which when run on a computer causes the computer to perform
the method according to the second aspect.
[0045] In a fourth aspect, the invention provides a carrier medium
carrying computer readable code which when run on a computer causes
the computer to perform the method according to the second
aspect.
[0046] In a fifth aspect, the invention provides computer program
product comprising computer readable code according to the fourth
aspect.
[0047] In a sixth aspect, the invention provides an article of
manufacture comprising:
[0048] a. machine-readable storage medium; and
[0049] executable program instructions embodied in the machine
readable storage medium that when executed by a programmable system
causes the system to perform the function of processing utility
consumption data comprising the steps of: [0050] receiving utility
consumption data; [0051] deriving at least one fact from utility
consumption data; [0052] storing the at least one fact in a fact
memory; and [0053] inferring at least one new fact from the at
least one fact stored in the utility consumption data.
[0054] In a seventh aspect, the invention provides a utility data
processing data system for deriving facts relating to consumption
of a utility comprising: [0055] a fact memory for storage of facts
relating to utility consumption received from one or more fact
sources; [0056] wherein one said act source comprises an inference
module configured to infer new facts relating to utility
consumption from one or more facts stored in the fact memory; and
[0057] a question management module configured to communicate a
request to a system user to provide one or more facts not available
from any other fact source.
[0058] In an eighth aspect, the invention provides method of
processing utility consumption data comprising the steps of: [0059]
analysing facts relating to utility consumption stored in a fact
memory to identify one or more required facts not contained within
the fact memory; and [0060] if one or more required facts is not
contained within the fact memory, communicating a request to a
system user to provide the one or more facts.
[0061] In a ninth aspect, the invention provides computer program
code which when run on a computer causes the computer to perform
the method according to the eighth aspect.
[0062] In a tenth aspect, the invention provides a carrier medium
carrying computer readable code which when run on a computer causes
the computer to perform the method according to according to the
eighth aspect.
[0063] In an eleventh aspect the invention provides a computer
program product comprising computer readable code according to the
tenth aspect.
[0064] In a twelfth aspect the invention provides an article of
manufacture comprising:
[0065] a machine-readable storage medium; and
[0066] executable program instructions embodied in the machine
readable storage medium that when executed by a programmable system
causes the system to perform the function of processing utility
consumption data comprising the steps of: [0067] analysing facts
relating to utility consumption stored in a fact memory to identify
one or more required facts not contained within the fact memory;
and [0068] if one or more required facts is not contained within
the fact memory, communicating a request to a system user to
provide the one or more facts.
[0069] "Facts" as used herein means facts relating to utility
consumption within a household and factors affecting utility
consumption. Facts include but are not limited to: number of
occupants in a household; geographic location of a household;
identity of appliances that consume the utility; duration of use of
appliances; time of day when utilities are consumed; duration of
use of appliances; cost of electricity consumed; and baseload.
[0070] "Baseload" as used herein means the minimum level of utility
demand during a defined period, which may be a fixed period such as
24 hours or a user-defined period such as waking hours.
BRIEF DESCRIPTION OF THE FIGURES
[0071] The invention will now be described in more detail with
reference to the Figures, wherein:
[0072] FIG. 1 illustrates a system for communicating utility
consumption data to a fact generator;
[0073] FIG. 2 illustrates a utility management system according to
an embodiment of the present invention;
[0074] FIG. 3 illustrates a control and display interface according
to an embodiment of the present invention;
[0075] FIG. 4 is a flow chart illustrating operation of a question
management module of the system according to an embodiment of the
present invention;
[0076] FIG. 5 is a flowchart illustrating measuring, analysis and
matching steps in an event identification process;
[0077] FIG. 6 illustrates the identification of "corners" in
electricity consumption data in an event identification
process;
[0078] FIG. 7 illustrates schematically the identification of
missing corners in an event identification process;
[0079] FIG. 8 is a flowchart illustrating the corner detection
algorithm in an event identification process; and
[0080] FIG. 9 illustrates a matching step in an event
identification process.
DETAILED DESCRIPTION OF HE INVENTION
[0081] FIGS. 1-3 describe a utility data processing system for
processing of electricity consumption data (although it will be
appreciated that analogous systems may be used in relation to
management of other utilities, such as water and/or gas
consumption).
[0082] With reference to FIG. 1, electricity consumption is
measured by sensor 101. A sensing device such as a clamp-on energy
meter as disclosed in WO 2008/142431 or as available as part of
Onzo Limited's smart energy kit, measures real and reactive power
at fixed time points ("real power" and "reactive power" as used
herein have the meanings as understood by a skilled person in the
art in relation to power supplied to a load from an alternating
current source). Electricity consumption may be measured at least
once every 60 seconds, for example once every second, and the
measurements are captured as two separate streams of real and
reactive power data. One advantage of measuring both real and
reactive power is that, between them, it is possible to measure
power demand of most or all appliances. For instance, it may be
difficult or impossible to obtain a meaningful measurement of real
power for certain appliances such as set-top boxes, however
reactive power for these devices can be measured.
[0083] Also measured is energy consumed at fixed time intervals,
typically every second. From this can be calculated a running total
of energy consumed over longer periods, for example every 512
seconds, 2048 seconds or 86,400 seconds (24 hours). These
measurements can also be used to show the maximum and minimum
energy usages over one of these longer time periods.
[0084] The high "granularity" of data obtained from a relatively
high frequency of measurement as described may yield a larger
number and higher accuracy of derived or inferred facts, however it
will be appreciated that derived or inferred facts can likewise be
generated from a lower frequency of measurement such as once every
hour, once every day or even lower frequency.
[0085] The measured data is communicated wirelessly to a user
display 102, such as the portable display available in Onzo's smart
energy kit. To enable wireless communication, the sensor may be,
for example, a ZigBee end device (ZED). The display can show
information such as current power consumption, daily total energy
consumption, comparison to previous weeks, target energy
consumption and current and cumulative cost of electricity
consumed. Where the sensor is a ZED it may be a device as disclosed
in PCT/GB2010/051707.
[0086] The display 102 may communicate by a wired or wireless link
with interface 103, such as a user's PC, to upload the measured
electricity consumption data via, a utility management website.
[0087] In addition to, or as an alternative to, uploading the
electricity consumption data through the display 102, the data may
be communicated wirelessly directly from the sensor. For example,
in the case where the sensor is a ZED, the data transmitted from
the ZED may be communicated to a ZigBee Ethernet gateway and from
there to a router.
[0088] Utility consumption data may be communicated by any other
means known to the skilled person, for example by use of automated
metering infrastructure (AMI) or automatic meter reading (AMR)
systems.
[0089] Control of the communication of data may be under the
control of one or more of a system user such as a household
occupant; a utilities provider; and a utilities service provider,
such as a provider of utility data processing services.
[0090] Uploaded data is analysed by utility data processing system
104 to generate facts relating to utility consumption, and
information generated from these facts may be communicated back to
the user on interface 103, downloaded to display 102, or presented
to a user on a different interlace. This interface presents
information relating to utility consumption that enables a user to
make decisions on how to manually manage utility consumption.
Additionally, the interlace may enable automated control of utility
consumption.
[0091] FIG. 2 illustrates the utility data processing system in
more detail. In overview, the system takes information from one or
more different sources, such as measured household electricity
consumption data or facts about the household declared by a user,
and uses this data to derive further facts about the household,
such as appliances present within the household. In this way, it is
possible to build up a detailed profile of energy consumption
within the household that can be used to inform a user of energy
consumption within the household and/or to control energy
consumption.
[0092] For example, the present inventors have found that one set
of facts that may be derived from the electricity consumption data
is the identity of electrical appliances within the household, and
with this set of facts the system can identify further facts
relating to occupancy and activity within the household based on
patterns of usage of those identified appliances. Although the
accuracy of inferred facts may be verified by a user of the utility
data processing system, such as an occupant of the household, the
utility data processing system is operable to generate a highly
detailed profile of the household without necessarily requiring any
input at all from a user, or requiring only limited user input.
[0093] The utility data processing system comprises a data store
210, a facts store 240 containing facts derived from data stored in
data store 210, a display interface for presenting facts to a user,
and a control interface for automated control of electricity
consumption. The display interface and control interface are
combined in a control/display interface 280. Facts are generated
using inference system 224.
[0094] Each of these components is described in more detail
below.
[0095] Data Store
[0096] Household electricity consumption data 201 is stored in data
store 210. This electricity consumption data may be from sensor 101
as described above with respect to FIG. 1, or from any other
electricity consumption measurement source wherein electricity
consumption at a plurality of time points has been measured.
Non-household electricity consumption data 202 may also be stored
in the data store 210. Non-household electricity consumption data
202 may include data such as National Grid power consumption data
by substation.
[0097] The household electricity consumption data comprises a
plurality of electricity consumption values at a corresponding
plurality of measurement points, as measured by and transmitted
from the sensor 101 as described above.
[0098] Analogously, household gas and/or water consumption data
from household gas and/or water meters may also be stored in the
data store 210. Water and gas consumption, in particular water
consumption, may be measured at a lower rate than electricity
consumption, for example at least once every 300 seconds or at
least once every 60 seconds, in order to generate water consumption
data. The rate of flow of water or gas at each time interval may be
measured, along with the total volume consumed over time in a
manner analogous to power and energy measurements of electricity
consumption. Additionally or alternatively, water and gas
consumption may be measured at measurement points after intervals
of volume consumption rather than intervals of time, for example a
measurement of time elapsed for each unit volume (e.g. litre) of
water to be consumed.
[0099] If household gas and/or water consumption data is stored
then in the data store 210 then non-household gas and/or water
consumption data may also be stored in the data store 210.
[0100] Inference module 224, statistics module 230 and appliance
and event detection module 222 are used to populate fact store 240
with facts, as described in more detail below.
[0101] Appliance and Event Detection Module
[0102] Appliance and event detection module 222 is configured to
analyse the electricity consumption data to identify changes in
electricity consumption over time, in particular changes in real
and/or reactive power, that are indicative of specific devices. For
example, the event of a refrigerator switching on entails an
identifiable series of power changes characteristic of that event.
In this way, the appliance and event detection module 222 is able
to detect and identify "signature" events associated with operation
of an appliance. In this way, the identity of appliances present
within the household can be derived from electricity consumption
data, and fact store 240 can be populated with this information.
Moreover, the electricity consumption data stored in data store 210
includes data relating to the time of measurement and so appliance
and event detection module 222 is also able to identify the time at
which events occur, for example a kettle or television being
switched on or off. The identity of appliances in the household may
be derived from consumption data using techniques disclosed in
PCT/GB2010/002093.
[0103] Once an appliance has been identified based on a signature
event associated with that appliance, the detection module 222 may
analyse future electricity consumption data in search of that event
to build up a picture of how that appliance is used over time.
Doing this for a single appliance or multiple appliances can be
used to build up a detailed pattern of energy usage within a
household.
[0104] Inference Module
[0105] In operation, inference module 224 instructs appliance and
event detection module 222 to analyse the electricity consumption
data in search of a specific appliance, or event associated with
operation of an appliance. Initially (for example if fact store 240
is empty) detection module 222 may be instructed by inference
module 224 to search for appliances that are present in most
households, for example a refrigerator or a television. If
detection module 222 identifies the presence of a given appliance,
inference module 224 may then instruct detection module 222 to
search for other appliances that are likely to also be present in
view of the presence of that given appliance. For example, if a
television is identified then inference module 224 may instruct
detection module 222 to search for a DVD player and/or a set-top
box.
[0106] The inference module 224 also generates inferred facts 246
that do not necessarily involve use of the appliance and event
detection module. For example, if an existing fact in the fact
store 240 is that a DVD player and/or a set-top box is present in
the household then it may be inferred that a television is also
present in the house without necessarily requiring the appliance
and event detection module 222 to search for that appliance.
[0107] Statistics Module
[0108] The statistics module 230 generates a variety of statistical
facts 232 based on the measured electricity consumption data
including:
[0109] i) Average energy usage. This may be average usage for any
given time period such as a day, week or month, or average usage
for a given point such as a day of the week.
[0110] ii) Times of maxima and/or minima in energy usage.
[0111] iii) Average cost of electricity, calculated using energy
supplier tariffs in combination with electricity consumption
data.
[0112] In relation to each of (i), (ii) and (iii) above, the
statistical facts generated may be in relation to energy
consumption within the household as a whole and/or in relation to
one or more specific appliances.
[0113] Initially, when fact store 240 is empty, statistics module
230 may be used to initiate its population, for example by adding
facts relating to energy usage per time period, identifying facts
relating to variation of consumption, such as variation in
consumption over the course of a week; and determining baseload
energy consumption.
[0114] In addition to generating statistical facts from energy
consumption data, the statistics module 230 may also use data
relating to national average electricity consumption and
statistical facts to provide a user with a comparison of household
energy consumption with the national average. Alternatively or
additionally, comparison may be made with one or more peer groups
such as households of a similar size; households of similar
occupancy; or households within the same locality, such as
households within the same village, town or city. The statistics
module can be used to set targets for reduction in energy
consumption. The target may be a target energy consumption over a
given period of time (such as 1 week or 1 month) that is a defined
percentage lower than the measured household energy consumption for
a previously measured period of time. Moreover, the target may
relate to one or more specific appliances, such as appliances that
consume a high percentage or disproportionate amount of the total
energy consumed, rather than a target in relation to the household
as a whole. Alternatively or additionally, the target may be to
match or fall below the national average electricity consumption,
or electricity consumption of one or more selected peer groups.
[0115] The statistics module 232 may also be used to set tips for
energy consumption. This may be, for example, a tip suggesting that
energy be used less at a specific time of day when national or
regional energy demand is high, if household energy demand at that
time is high.
[0116] In the embodiment of FIG. 2, statistics module 230 serves to
both generate statistical facts 232 using electricity consumption
data 201; to generate statistics in which electricity consumption
within the household is compared with the national average or a
peer group; and to generate targets or tips for energy consumption.
However, it will be appreciated that generation of statistical
facts 232, generation of statistics and generation of targets
and/or tips may each be carried out by separate modules of the
system.
[0117] Fact Store
[0118] The fact store 240 stores the following:
[0119] i) Declared facts 242, for example a declaration from a user
of the utility data processing system, such as a household
occupant, that a certain appliance is present within the household;
information on geographic location of the household; information on
size of the house such as number of bedrooms and bathrooms; and
information on the number of household occupants.
[0120] Facts may be declared in response to a prompt by the utility
data processing system. For example, a user of the system may be
asked to verify if an appliance identified by the appliance and
event detection module 222 is indeed present in the household.
Alternatively, a user may provide facts unprompted, for example a
user may identify the presence of an appliance that has not been
detected by the appliance and event detection module 222.
[0121] ii) External facts 248, which may be from one or more
external fact sources 250, for example information on energy
consumption by commercially available appliances and information on
the household electricity tariff. External facts 248 may also
include facts that are not provided as declared facts 242 but that
are available from other sources, such as the address of the
household.
[0122] iii) Derived facts, which includes:
[0123] Event facts 244 derived from appliance and event detection
module 222, such as the identity of an appliance within the
household that is derived from household electricity consumption
data by operation of the appliance and event detection module 222.
Event facts also include time, energy consumption and duration of
use of appliances (identified either by declaration or
derivation).
[0124] Statistical facts 232 derived from statistics module 230 as
described above, including but not limited to energy use per day,
week, month or year; variations in energy usage on different days
of the week; variations in energy usage during different hours of
the day; baseload energy; and periods when energy usage is a
defined percentage above the baseload level for a defined period of
time.
[0125] iv) Inferred facts 246, that are facts that can be inferred
from existing facts in the fact store 240, as described above with
respect to operation of inference module 224.
[0126] Each one of the aforementioned facts may be a "final fact",
an "intermediate fact" or both.
[0127] A final fact is a fact that is presented to a system user
and/or used in determining a control parameter in the case of
automated utility consumption control. For example, the fact that a
refrigerator is determined to be present in the household may be a
final fact.
[0128] An intermediate fact is a fact that is used to generate
further facts, such as a fact that causes inference module 224 to
seek further facts based on the intermediate fact. For example, the
fact that a television is determined to be present in the household
may be an intermediate fact that causes the inference module 224 to
infer the presence of a television as a further fact and or to
direct appliance and event detection module 222 to analyse utility
consumption data in order to determine if a related appliance such
as a television is present in the household. It will be appreciated
that an intermediate fact may also be a final fact.
[0129] Similarly, an intermediate fact may cause the system to
present a question to a system user wherein the answer to the
question is stored as a declared fact.
[0130] Confidence
[0131] It will be appreciated that a declared, event or inferred
fact may be incorrect. For example, it will be appreciated that
declared facts may be incorrect. In the case of event facts, the
appliance and event detection module 222 may identify an appliance
which is in fact not present within the household. A confidence or
probability may be associated with each fact, and if that
confidence does not exceed a threshold (for example, at least 50%)
then the associated fact may be disregarded for use as an
intermediate or final fact.
[0132] An inferred fact generated from two or more existing facts
may have a confidence based on confidence of the starting acts. For
example, combination of two facts both having a confidence of 50%
may give an inferred fact with a confidence of 25%. Alternatively,
a combination of facts may produce an inferred fact having a
confidence based on other factors. For example, if it is known that
60% of nationwide or peer-group households with a TV and DVD player
also possess a digital set-top box then determining that a TV and
DVD player are present within a household may give a confidence
that a digital set-top box is present of no more than 60%.
[0133] In the event of a discrepancy between two facts, the fact
with the highest confidence may be taken to be the accurate
fact.
[0134] The confidence associated with any given fact may be
increased or decreased by use of other facts. For instance, in the
example given above of a digital set-top box, the confidence of the
inferred fact may be increased by using the event identification
process to seek a signature corresponding to a set-top box.
[0135] Additionally, sensors within a household may provide further
data that can be used to adjust confidence for any given fact,
including but not limited to the following: [0136] Water
consumption for appliances such as washing machines. For example,
the confidence of a fact that a washing machine is present in a
household may be increased not only by identifying an electrical
event series associated with operation of a washing machine but
also by determining if water was consumed at the same time as
electricity was consumed for the relevant event series. In a more
sophisticated analysis, measurement of a household's water demand
over time may be used to identify water consumption signatures of
appliances present in the household that can be matched to known
water consumption signatures of known appliances. For instance, a
water consumption signature may be based on changes in rate of
water consumption with time in a manner analogous to electrical
appliance signatures. [0137] Gas consumption for appliances that
consume both gas and electricity in the same way as water
consumption described above. [0138] Temperature of appliances that
change temperature with use, for example refrigerators, freezers
and boilers. [0139] Temperature difference between household
ambient temperature and external temperature. [0140] Movement
sensors such as passive infrared sensors to determine times at
which a household is occupied for correlation with other facts
relating to appliances in the household and their time of use.
[0141] Probability data in particular data derived from:
[0142] (i) Socio-economic data, seasonal data and/or geographic
data. For example the confidence in an inferred, derived or event
fact may vary based on the nature of the household (e.g. domestic
or office), the geographic location of the household and the
household peer group. With respect to location of the household,
factors affecting the confidence of a fact, such as facts relating
to presence of a given appliance being present in a given
household, may include demographics of household residents in the
area in which the household is located. Similarly, the climate in
the location of the household may have an effect on confidence
associated with a fact. For instance, confidence associated with
facts relating to presence and use of heating appliances may be
higher in relatively cold climates whereas confidence associated
with facts relating to presence and use of cooling appliances may
be higher in relatively warm climates.
[0143] (ii) Behavioural data. For example, the confidence of an
event fact associated with operation of a lawnmower may be
increased if the relevant event series occurred during the day,
however confidence may be decreased if the relevant event series
occurred at night. The use of certain appliances may also vary over
the course of a year and from season to season, and this variation
may also be taken into account in determining a confidence. For
example, the confidence that a heating appliance is present and in
use in the household may be higher in the winter than in the
summer, and vice versa for a cooling appliance. The variation in
frequency and intensity of use of such appliances over the course
of a year may be taken into account.
[0144] Fact and Data Display
[0145] Utility consumption data from the data store 210 and facts
from the fact store 240 are presented to a user at interface 280.
The interface may be any form of interlace, such as a web interface
or other interface capable of receiving and displaying data from
the utility data processing system. For example, the interface may
be a touch-screen display located within the household that is
configured to receive and display data from fact display module 260
and data display module 270.
[0146] Interface 280 is capable of sending information back to the
system, such as declared facts 242 that may be unprompted inputs
made by a user or may be in responses to questions generated by the
system. In the case of automated utility management, information
sent back to the system may be control instructions such as control
parameters to be applied by the system.
[0147] Fact display module 260 and data display module 270 are
described in FIG. 3.
[0148] The fact display module 260 is configured to present facts
at interface 280 using facts display 304 relating to appliances
consuming electricity during a chosen period of time such as:
[0149] identity of appliances consuming electricity during the
period; and [0150] for each identified appliance, the energy
consumed, duration of use, time of use and cost of electricity
incurred during the period.
[0151] The facts display module 260 may also provide indications of
changes that have been detected. For example, if an appliance is
showing an abnormal event series, such as higher than usual energy
consumption, then a user may be alerted to a possible fault in that
appliance.
[0152] Likewise, the data display module 270 is configured to
present utility consumption data such as total energy consumed in a
given period, for example in kilowatt hours; times of maximum and
minimum energy usage during that period; and total cost of
electricity consumed during that period.
[0153] Statistics may also be presented using statistics 232 such
as: [0154] usage that is significantly higher or lower than the
national or peer average; and [0155] usage that is significantly
higher or lower than the historical household average.
[0156] This may be presented in numerical format or in the form of
a display using graphing display module 302. These usage statistics
may be for any given period, for example for any selected day,
week, month or year. Alternatively, these statistics could be
presented as text, such as "your electricity consumption during
[selected day or month or week or year] is [much
lower/lower/higher/much higher] than [the national average/peer
group average/household average]". Comparisons may be made based on
calendar periods as an alternative to or in addition to an overall
average. For example, consumption for the month of January may be
compared to overall consumption and/or consumption for the previous
January or Januaries to take account of seasonal variations in
utility consumption. Likewise, comparisons may be made between
weekends and weekdays rather than entire weeks, or for specific
days, to take account of different utility consumption patterns
across these different periods.
[0157] The presentation of data may be used for purposes other than
management of utility consumption.
[0158] For example, a pattern of utility consumption may be
determined by monitoring utility consumption over time. This
pattern may be stored as indicative of normal utility consumption
within the household for a defined period, such as 24 hours (such
as a pattern indicative of normal utility consumption on weekdays
and a pattern indicative of normal utility consumption on
weekends). A user may be alerted if energy usage varies from this
pattern.
[0159] For example, a security service may be provided wherein the
pattern includes a period during weekdays when utility consumption
is low, indicative of an empty household. A system user may be
alerted to variations to this pattern, such as utility consumption
at any point in time during this empty period that exceeds normal
levels by a predetermined multiple of the normal level at that
point in time.
[0160] Similarly, a service may be provided to vulnerable
individuals such as the elderly and/or individuals that live alone
wherein the detection of a change in a normal pattern of
consumption of utilities triggers an alert to check on the
wellbeing of those individuals. The change may be the occurrence or
absence of a specific detectable event, such as a failure to detect
use of a specific appliance within a specific time period, or a
failure to detect use of any appliances at all for a specified
length of time and/or within a specified time period.
[0161] Question Management Module
[0162] The utility data processing system is configured so as to
enable processing of utility consumption data by generating
accurate facts with a minimum of user input. User input may be
completely unnecessary, however the utility data processing system
may prompt direct user input to increase the number of facts and/or
to improve confidence in accuracy of facts. Exemplary questions
that may be made to a system user include but are not limited to
the following:
[0163] How many adults live in your home?
[0164] What type of air conditioning is there in your home?
[0165] How many rooms are bathrooms?
[0166] How many rooms are bedrooms?
[0167] Do you have cavity wall insulation?
[0168] How many people under 18 live in your home?
[0169] Are any people at home during the day?
[0170] How many of your windows are double glazed?
[0171] How many storeys do you have?
[0172] Do you have floor insulation?
[0173] Do you have a food processor?
[0174] Do you have an electric fryer?
[0175] Do you have a games console?
[0176] Do you have a garden pond?
[0177] FIG. 4 illustrates a flow chart in which facts (Fact 1, Fact
2, Fact 3 . . . Fact n) are checked to determine if those facts are
present in the fact store. If a fact is found in the fact store
240, as illustrated in FIG. 4 as "Y" for Facts 1 and 2, then no
further action is taken. If, however, one or more facts has not
been obtained from any other fact source available to the system,
illustrated by "N" for Fact 3, then questions management module 309
prompts a user with a question to provide that fact or facts to be
stored in fact store 240 as a declared fact.
[0178] The prompt may be displayed on a display that is located
within the household for an occupant of the household to respond
to, such as display 102, and/or it may be transmitted from the
utility data processing system in the form of an email, text
message, secure web page or other communication means to a
registered user of the system.
[0179] The questions module 309 may be operated not only when the
utility data processing system is first used but also on an ongoing
basis to identify changes that need to be reflected in the contents
of the fact store 240. For example, if energy consumption has
fallen significantly then this may prompt the questions management
module to ask a user if household occupancy has changed, which may
affect the household peer group. Similarly, if the event and
appliance detection module 222 identifies the absence of an event
within the household that had previously been regularly recurring,
or the presence of a different event in its place, then question
management module may ask a user if the relevant household
appliance has been removed or changed. A significant increase or
decrease in household energy consumption for a given period (for
example, an increase or decrease that is a preselected percentage
above or below the household average for the period in question),
or other disruption to an established pattern of utility
consumption, may also prompt the system to enquire if there have
been any changes in the household causing this change in utility
consumption.
[0180] Target Management Module
[0181] Target management module 307 provides the user with detailed
information on actual vs. target consumption, which may be
generated using statistics as described above, and may generate a
list of tips or "to do" items that will serve to reduce household
energy consumption, particularly in areas in which energy
consumption is relatively high compared to national, peer group or
historic household averages, such as the following:
[0182] Its easy to forget when you switched the oven on to
pre-heat. But it could be costing you a fortune. So watch your
time!
[0183] Boiling excess water wastes money. Measure the amount you
need with a cup mug or teapot before switching the kettle on
[0184] Loading dishes in their proper places will maximise cleaning
efficiency
[0185] Use a brush rather than a vacuum to clean floors
[0186] Did you know that up to 35% of heat can be lost through your
walls? Insulation can make a huge difference to your heating
bill
[0187] "Draught-proofing windows, doors and floorboards is cheap
and effective"
[0188] Fix bubble wrap to the back of the loft door and it will
stop heat escaping into the loft
[0189] "Washing clothes at 30.degree. C. instead of a higher
temperature can use significantly less electricity!"
[0190] Modern powders and detergents work just as effectively at
lower temperatures
[0191] Washing with a full load is much more efficient than washing
several smaller loads
[0192] "Look out for cycles such as `quick wash` for clothes that
aren't very dirty and just need a freshen up, or `1/2 load` if the
drum's not full"
[0193] "Washing less frequently save detergent, water and
electricity and your clothes will last longer!"
[0194] Keep an eye out for the energy label and try to buy the most
energy efficient model you can
[0195] Switching appliances off at the wall saves you energy and
money
[0196] Fluff in the filters makes your tumble dryer use more
energy. Clean them now and then to save money
[0197] "Overfilling your dryer stops the air circulating properly
and your clothes will take longer to dry, using more energy"
[0198] Utility consumption facts may also be passed to utility
suppliers or other third parties via API 290 subject, as necessary,
to control of confidential information.
[0199] Control
[0200] The utility data processing system of the invention may, in
addition to or as an alternative to displaying facts about utility
consumption, use the facts to control appliances via interface 280.
In particular, one or more appliances may be automatically switched
on or off or otherwise adjusted if one or more pre-determined
parameters are met such as user pre-approval for such control.
[0201] For example, the utility data processing system may be
configured to minimise utility consumption at a time when the
system determines that the household is unoccupied; to reduce
consumption by appliances that are identified as non-essential; or
in order to meet a utility consumption target.
[0202] Implementation of automated control may be using building
management systems (BMS) known to the skilled person.
[0203] Event Identification Process
[0204] Operation of the event detection module 222 referred to
above is described here in more detail.
[0205] Measurement
[0206] A sensing device such as a clamp-on energy meter as
disclosed in WO 2008/142431 measures real and reactive power at
fixed time points. A higher frequency of measurement will obviously
yield more electricity consumption data, which in turn increases
the likelihood of an accurate match when the profile generated from
the measured data is compared to stored electricity consumption
profiles. Typically, electricity consumption is measured at least
once every second. This is captured as two separate streams of data
("real power" and "reactive power" as used herein have the meanings
as understood by a skilled person in the art in relation to power
supplied to a load from an alternating current source). One
advantage of measuring both real and reactive power is that,
between them, it is possible to measure power demand of most or all
appliances. For instance, it may be difficult or impossible to
obtain a meaningful measurement of real power for certain
appliances such as set-top boxes, however reactive power for these
devices can be measured.
[0207] Also measured is energy consumed at fixed time intervals,
typically every second. From this can be calculated a running total
of energy consumed over longer periods, for example every 512
seconds, 2048 seconds or 86,400 seconds (24 hours). These
measurements can also be used to show the maximum and minimum
energy usages over one of these longer time periods. Although these
energy consumption measurements are not used in generating an
"event matrix" as described in more detail below, this information
is nevertheless beneficial in providing a detailed picture of
energy consumption over the course of an extended time period
during which various appliances may be switched on and off.
[0208] Consumption of water and gas can be measured using
techniques that are well known to the skilled person, for example
based on use of water and gas meters. Water and gas consumption, in
particular water consumption, may be measured at a lower rate, for
example at least once every 300 seconds or at least once every 60
seconds, in order to generate water consumption data that may be
used to identify events associated with consumption of water. The
rate of flow of water or gas at each time interval may be measured,
along with the total volume consumed over time in a manner
analogous to power and energy measurements of electricity
consumption. Additionally or alternatively, water and gas
consumption may be measured at measurement points after intervals
of volume consumption rather than intervals of time, for example a
measurement of time elapsed for each unit volume litre) of water to
be consumed.
[0209] Compression
[0210] As shown in FIG. 5, the electricity consumption data
relating to real and reactive power is fed into a compression
algorithm, referred to hereinafter as a "corner detection
algorithm". The compression and corner detection may be carried out
as disclosed in PCT/GB2010/002092 and/or PCT/GB2010/002093.
[0211] The operation of the corner detection algorithm is
illustrated schematically in FIG. 6. The compression algorithm
identifies "corners" in power demand by identifying differences in
the gradient representing rate of change in power from one time
point to the next. A point at which there is change in gradient
between two time intervals (identified as T(2), P(2)) is marked as
a "corner" if that change is greater than a predetermined
threshold. This is done by measuring the power difference between
points T(3), P(3) and T(2), P(2) and between T(2),P(2) and
1(1),P(1) to give values A1 and A2 respectively. If the difference
B between A1 and A2 exceeds a predetermined value Tol1 then a
corner is marked.
[0212] The operation of the algorithm is illustrated in more detail
in FIG. 8 in which:
[0213] T(x), T(i) and T(j) represent 32 Bit timestamps
[0214] C(x), C(j) and Y(i) represent 16 Bit power readings at a
corner
[0215] Tol1, Tol2 represent integer numerical values (0-100)
[0216] A1, A2, B represent 16 Bit power reading differences
[0217] n1, nMax, nMin, n2 represent 16 Bit numerical values
[0218] M(i), M(i)max represent 16 Bit numerical values
[0219] Section 401 of FIG. 8 illustrates identification of corners
as described above with reference to FIG. 6.
[0220] Section 402 of FIG. 8 illustrates the classification of
corners into "Standard" and "Fine" classes depending, respectively,
on whether B is greater than predetermined values Tol1 and Tol2 or
greater than Tol1 only.
[0221] The skilled person will understand how to select the value
of the threshold for marking a point as a corner, and the specific
value will vary from case to case. In order to avoid incorrect
identification of background noise in identification of a corner,
only those points having a signal strength greater than a minimum
multiple of background noise strength may be used in corner
identification.
[0222] By measuring a plurality of these corners in the electricity
consumption data, an electricity consumption profile is generated,
representing a series of events associated with changes in power
demand from which appliances may be identified using known
"signature" profiles of those appliances.
[0223] Correction
[0224] The electricity consumption profile generated as described
above with respect to FIG. 6 and sections 401 and 402 of FIG. 8
contains the majority of corners, however a correction may be
applied to identify one or more corners that may have been
missed.
[0225] This is illustrated in FIG. 7 which shows a corner C(2)
between corners C(1) and C(3) that has been missed by the corner
detection algorithm.
[0226] A missing corner may be identified if both the power
difference (power at C1-power at C2) and the time difference (time
at C1-time at C2) fall outside defined values as illustrated in
section 403 of FIG. 8.
[0227] In this event, a linear interpolation may be conducted to
identify any missing corners, as illustrated in Section 403 of FIG.
8. Referring to FIG. 3, missing corner C3 should be inserted at the
point giving the most acute angle between lines C1-C2 and
C2-C3.
[0228] Matching
[0229] The electricity consumption profile may be represented in
the form of a matrix representing the various events that occurred
during the period that electricity consumption was measured. For
example, if a refrigerator was switched on during the period of
electricity consumption measurement then the signature profile
associated with that event may be as follows:
Time ( 1 0 4 7 ) Event Start Finish ##EQU00001##
[0230] The electricity consumption profile may be analysed to
determine if it contains a signature event series stored in a
database indicative of a refrigerator switching on. The event
series of one or more candidate appliances stored in a database may
be compared to the measured profile in order to identify whether
the measured profile contains an event series matching that of a
refrigerator switching on.
[0231] The matrix below illustrates the output from matching of a
number of candidate events to the above event matrix. In this case,
the first row shows a good match between the measured profile and
the event to which it was compared. The further rows show poorer
matches, indicating that the measured event probably corresponds to
the candidate event of the first row.
Time ( 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 1 ) Event Start Finish
##EQU00002##
[0232] In this way, the probability of a given appliance being
present in the household can be determined. If this probability
exceeds a threshold value then that appliance is assumed to be
present in the household. If on the other hand the probability
fails below a threshold then that appliance is assumed not to be in
the household. For instance, an event series represented by one row
of the matrix may form a good match with the event series of an
appliance in the database, and so the data in that row can be
attributed to an identified appliance.
[0233] Parameters associated with an event that may be used to
determine a match include the following:
[0234] Minimum change in power
[0235] Maximum change in power
[0236] Peak power minimum
[0237] Peak power maximum
[0238] Minimum power change time after time 0
[0239] Maximum power change time after time 0
[0240] Minimum time to next event
[0241] Maximum time to next event
[0242] Power threshold (the minimum power change between
measurement points). These parameters may be determined by
measuring event series of known appliances. Each of these
parameters may be determined for each specific make and model of an
appliance and/or may be a generic parameter to be used for any
member of a genus of appliances (for example, the genus of washing
machines) wherein the generic parameter is determined by measuring
a plurality of devices within a genus and determining a parameter
value that is applicable to most or all members of the genus.
[0243] This is illustrated schematically in FIG. 9, which
illustrates a matching step wherein a match may be based on one or
more of an event occurring at a first time point between T1 and T2;
the event having an associated power falling between P1 and P2; and
a time T until the next event occurring at a time point between T3
and T4.
[0244] Weighting may be applied to the event matrix in order to
improve the accuracy of matching. For example, one entry within the
event matrix may be associated with high power consumption that may
be given higher weighting than other entries that may be lower
power, and that may be easier to confuse with background
electricity consumption.
[0245] For any given event, the event series contained within the
database comprises a plurality of entries showing at least one
power change indicative of an event. Typically, the event comprises
a plurality of changes in power value, the magnitude and duration
of which can be used to generate a signature event series for that
event. However, it is possible that a single change in power value
could be stored as an event series within the database, in
particular if the magnitude of that change is large enough to
provide a distinctive event series.
[0246] Different techniques can be used in identification of
different appliance genera. Suitable techniques for any given genus
will be apparent to the skilled person following measurement and
analysis of event series for known appliances within each
genus.
[0247] For example, a kettle profile can be characterised by an on
event, followed by a similar off event after a defined amount of
time has elapsed (this amount of time is defined by parameters
derived from typical kettle usage). Since kettles may be of
different sizes, abroad search in on event magnitudes and duration
is firstly carried out in the electricity consumption data for the
whole household, yielding matrices with all appliances satisfying
those parameters. These results, since they tend to be different
instances of appliance usage, tend to form clusters when one plots
the on event power magnitude against the duration in time between
their respective on and off events. Then a finite mixtures model
based clustering method may be used to automatically select the
most likely candidate kettle cluster. Other attributes can be
calculated from this information, such as an estimate of the amount
of water boiled in each kettle use.
[0248] On the other hand, in the case of a washing machine, a
plurality of discrete events can be identified in relation to
operation of a washing machine, such as heating and spin cycle
events. Individually, these are simple on/off events, which can be
detected individually. Independent analysis of these individual
components can then be combined and further analysed to identify a
washing machine. Using this information, one can then use a similar
method of clustering to determine which is the most likely heating
element which corresponds to the washing machine, in order to
calculate the energy used, and temperature of the wash cycle, for
example.
[0249] Thus, in one embodiment an appliance may be detected by
identifying an event associated with only part of the operation of
that appliance. In particular, by measurement of event series
associated with known devices, certain events may be identified as
being more prominent than other events (for example due to a power
change associated with that event that is particularly
characteristic of the appliance genus in question) and may be used
as the primary event in the process of appliance detection. Other
events determined to be associated with the detected appliance may
be used to verify the accuracy of the match and/or to determine
parameters such as the amount of energy used by the appliance.
[0250] Thus, using information derived from measurement and
analysis of electricity consumption profiles of known appliances,
the skilled person may identify mathematical techniques suitable
for identification of any given appliance or any appliance genus
such as the finite mixtures model based clustering technique
described above, or other techniques such as hidden Markov
models.
[0251] In this way, use of known appliance event series for common
household appliances allows estimation of most or all of the
constituents of measured electricity consumption data for the
entire household, and allows for disaggregation of signals
associated with operation of more than one appliance.
[0252] The appliance may be any appliance to which power is
supplied via mains electricity including but not limited to kitchen
appliances such as fridges, freezers, microwave ovens, electrical
cookers, washing machines, tumble dryers and dishwashers; leisure
appliances such as televisions, hi-fis, set-top boxes, video or dvd
players or recorders; game consoles; and other appliances such as
electric boilers, central heating water pumps, pool pumps, air
conditioning units, personal computers, vacuum cleaners, irons and
lawn mowers.
[0253] Following an initial match of a measured event series to an
event series stored in the database associated with a specific
appliance, the confidence in accuracy of identification of that
appliance may be increased by looking for one or more further event
series corresponding to other events associated with operation of
that appliance. Furthermore, certain appliances may have event
series that occur cyclically, for example a refrigerator. If a
match is identified for such an appliance then the match may be
verified by checking for this cyclic pattern.
[0254] A detected event is not necessarily associated with
operation of an appliance. For instance, a power cut or other
abnormality in the power supply may possess an identifiable event
series.
[0255] Databases
[0256] As set out above, the event series of one or more candidate
appliances stored in a database may be compared to the measured
profile in order to identify whether the measured profile contains
a profile matching that of a stored event series.
[0257] One or more databases may be checked for the purpose of
event matching, including but not limited to:
[0258] i) A specific appliance database comprising event series
associated with specific appliances, such as event series for a
plurality of different makes and models of appliances. A database
of this type has the advantage of being able to determine very
specifically the make and model of devices present in a
household.
[0259] ii) A generic appliance database comprising event series
that are common to genera of devices. For example, the genus of
washing machines will all have similar event series such as event
series associated with heating or spin cycles, and those similar
event series may be used to create a single, generic event series
for the event in question. By measuring event series for a
plurality of different makes and models of devices it is possible
to identify appliance genera and at least one generic event series
common to each member of an appliance genus. Use of a generic
database of this type has the advantage of being smaller than a
database containing event series for a plurality of makes and
models of appliances within each genus, and also allows for faster
matching.
[0260] Initially, a generic event series may be generated using
only two event series associated with two different appliances of a
common genus, such as the spin cycle of two different models of
washing machine. In order to increase matching accuracy for a given
event or appliance, each generic event series may be changed over
time as event series for known appliances within the same appliance
genus are measured.
[0261] iii) A household database specific to the household for
which utility consumption data is analysed. Following the initial
identification of events occurring within a household and the
associated appliances, a database specific to that household may be
created, and the contents of this database may be used to quickly
identify future events based on previously measured occurrences of
those events or previous identification of the associated
appliance. The speed and accuracy of event matching using a
household database may increase as utility consumption data is
measured over time. Use of the household database in this way
allows for real-time identification of events and appliances, which
may be communicated to a system user either local to or remote from
the household.
[0262] Although the invention has been described above with respect
to electricity consumption, it will be appreciated that analogous
analysis, outputs and controls may also be applied to consumption
of water and gas.
[0263] Although the present invention has been described in terms
of specific exemplary embodiments, it will be appreciated that
various modifications, alterations and/or combinations of features
disclosed herein will be apparent to those skilled in the art
without departing from the spirit and scope of the invention as set
forth in the following claims.
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